Published online 6 March 2006
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Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods
1International Agency for Research on Cancer Lyon, France 2Department of Bioinformatics and Computational Biology, George Mason University Manassas, VA, USA 3Department of Clinical Oncology, Institute of Development Aging and Cancer, Tohoku University Sendai 980-8575, Japan
*To whom correspondence should be addressed at Genetic Susceptibility Group, Genetics and Epidemiology Cluster, International Agency for Research on Cancer, World Health Organization, 150 Cours Albert-Thomas, 69372 Lyon Cedex 08, France. Tel: +33 47273 8512; Fax: +33 47273 8388; Email: tavtigian{at}iarc.fr
Received December 23, 2005. Revised February 3, 2006. Accepted February 3, 2006.
Prediction of the biological effect of missense substitutions has become important because they are often observed in known or candidate disease susceptibility genes. In this paper, we carried out a 3-step analysis of 1514 missense substitutions in the DNA-binding domain (DBD) of TP53, the most frequently mutated gene in human cancers. First, we calculated two types of conservation scores based on a TP53 multiple sequence alignment (MSA) for each substitution: (i) Grantham Variation (GV), which measures the degree of biochemical variation among amino acids found at a given position in the MSA; (ii) Grantham Deviation (GD), which reflects the ‘biochemical distance’ of the mutant amino acid from the observed amino acid at a particular position (given by GV). Second, we used a method that combines GV and GD scores, Align-GVGD, to predict the transactivation activity of each missense substitution. We compared our predictions against experimentally measured transactivation activity (yeast assays) to evaluate their accuracy. Finally, the prediction results were compared with those obtained by the program Sorting Intolerant from Tolerant (SIFT) and Dayhoff's classification. Our predictions yielded high prediction accuracy for mutants showing a loss of transactivation (
88% specificity) with lower prediction accuracy for mutants with transactivation similar to that of the wild-type (67.9 to 71.2% sensitivity). Align-GVGD results were comparable to SIFT (88.3 to 90.6% and 67.4 to 70.3% specificity and sensitivity, respectively) and outperformed Dayhoff's classification (80 and 40.9% specificity and sensitivity, respectively). These results further demonstrate the utility of the Align-GVGD method, which was previously applied to BRCA1. Align-GVGD is available online at http://agvgd.iarc.fr.
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